Diabetic Fracture: Differential Gene Analysis
Steve Pederson
22 May 2019
Data Description & Quality Assessment
- The initial files were single-end reads of 100bp
- Libraries appeared to be stranded and contained Total RNA-like properties, not polyA
- Library sizes were between 37,710,559 and 44,890,754 reads, which was considered as good
- Low levels of adapter content were found and removed using
AdapterRemoval. This is normal and occurs when a small number of fragments < 100bp are sequenced - Spikes in GC content at around 63% and 72% were noted as potentially indicative of incomplete rRNA removal
- This raised the question as to whether human rRNA sequences targeted rather than rat during rRNA depletion?
- rRNA-aligning reads were not removed from the fastq files as they will be ignored during summarisation at the gene-level. They will however be expected to align multiple times when aligning to the reference genome
- Reads were aligned to the Rnor6.0 genome sourced from Ensembl Release 96, using
STAR 2.7.0d- These alignments were only used for visualisation and inspection with regard to QC
- For Differential Gene Expression (DGE) analysis, reads were pseudo-aligned to the transcriptome from Ensembl Release 96 for summarisation at the transcript-level using
kallisto 0.43.1.kallistois able to proportionally assign multi-mapping reads across both genes and transcripts- This approach only counts reads which align to spliced transcripts, as contained in the reference transcriptome, with the correct strandedness. Intronic reads are not counted, and neither are reads which align to the opposite strand from the transcript
- These transcript-level counts were used for DGE analysis after being summarised to the gene-level. There was not sufficient read depth to attempt transcript-level analysis.
GC content of each library showing uneven and ‘spikey’ patterns. Peaks on the RHS indicate potential incomplete rRNA removal, whilst the other peaks indicate possible excessive PCR cycles and high levels of duplicated sequences.
Estimated duplication levels within each sample. Higher than expected sequence duplication rates were observed, particularly for sequences present greater than 10,000 times. Sample 179_F and 187_F were of particular concern, however this is also suggestive of incomplete rRNA removal
STAR Alignment summary showing very low levels of unique alignments, which is again suggestive of incomplete rRNA removal. Uniquely mapping reads are within the range usually seen after summarising to genes, indicating that final gene-level counts may be a little lower than is optimal.
- Manual inspection of the alignments also indicated that the number of PCR rounds during library preparation may have been slightly more than required.
- PCR duplicates are difficult to identify with 100% confidence in paired-end libraries, and are not able to be removed confidently at all in single-end libraries
- Gene expression estimates in the presence of a high duplication rate are quite stable for moderate to highly expressed genes, however expression estimates for low-expressed genes are less reliable
- As a result, low-expressed genes will be removed to avoid false positives.
After assignment to transcripts, the library sizes ranged between 6,954,273 and 14,565,667 reads
- This is a disappointing result, but still manageable
- These library sizes were lower than might have been expected after inspection of the STAR alignment rate. Inspection of STAR alignments revealed that a significant number of reads were not derived from a spliced transcript, or were aligned to the incorrect strand
Gene Expression Analysis
- The first step of the analysis was to summarise transcript-level counts to the gene-level
- Data was analysed using voom-precision weights, which allows for the assumption of normality despite counts being a discrete variable.
- This also allows for a weighted regression analysis to be performed in which lower-confidence samples can be down-weighted. Standard t-tests for differential expression can also be employed, with moderated variances to reduce false positives & false negatives
- Sample weights were calculated by assuming that all samples were replicates, which produces more conservative weights. Given that the vast majority of genes are not differentially-expressed, this is also a realistic assumption.
- A PCA was performed using counts transformed to log2 CPM (counts per million) which also contains a normalisation step.
- The first principal component captured 57.7% of the variance
- The first two components captured a total of 82.9%, confirming a strong signal in the data
- PC1 tracked strongly with library size which was slightly concerning
- The difference between treatment groups was clearly evident on PC2 which confirmed a strong biological signal within the experiment, despite PC1.
PCA with colours indicating treatment group and point sizes indicating sample weights. A trend from left to right along PC1 tracked somewhat with library size, with all points along PC1 appearing in order of library size for each respective treatment group. Point sizes are proportional to sample weights.
Correlation between library size and PC1. A simple regression line is also shown for illustrative purposes
Differentially Expressed Genes
Differentially expressed (DE) genes were detected using a slight variation from the common null hypothesis. Instead of testing \(H_0: \log FC = 0\) which tests for the point value zero, the null hypothesis was defined as being within the range [\(-\tau, \tau\)], with \(\tau\) set at the default value of \(\log_2 1.2\). This additionally saves filtering any detected DE genes based on fold-change, as is common practice in many workflows.
\[ H_0: |\log FC| \leq \tau \\ vs \\ H_A: |\log FC| > \tau \]
- After analysis, 2 genes were found to be differentially expressed using an FDR of 0.05.
- Using a less stringent FDR threshold of 20.0% gave weak-to-moderate support for 18 genes as potentially DE.
| ID | Name | logFC | AveExpr | t | P.Value | FDR | Location |
|---|---|---|---|---|---|---|---|
| ENSRNOG00000047551 | Pnpla2 | -7.192 | 0.1297 | -13.03 | 3.366e-06 | 0.02817 | 1:213997709-214002815:(+) |
| ENSRNOG00000050004 | LOC100909761 | 6.553 | -1.417 | 12.54 | 4.281e-06 | 0.02817 | 9:37727942-37747514:(+) |
| ENSRNOG00000032832 | Mmp10 | 3.502 | 2.164 | 8.801 | 3.739e-05 | 0.164 | 8:5734348-5742243:(+) |
| ENSRNOG00000032626 | Mmp3 | 3.612 | 4.589 | 8.212 | 5.869e-05 | 0.1722 | 8:5676665-5698579:(+) |
| ENSRNOG00000002802 | Cxcl1 | 4.895 | 1.902 | 7.867 | 8.313e-05 | 0.1722 | 14:18743685-18745457:(-) |
| ENSRNOG00000010278 | Il6 | 4.79 | 0.1518 | 7.816 | 8.615e-05 | 0.1722 | 4:3043231-3047807:(+) |
| ENSRNOG00000060849 | Rcor2 | -5.115 | -1.303 | -7.702 | 9.595e-05 | 0.1722 | 1:222519615-222524779:(+) |
| ENSRNOG00000000239 | Ccl7 | 2.751 | 2.899 | 7.271 | 0.0001171 | 0.1722 | 10:69423086-69424979:(+) |
| ENSRNOG00000007545 | Angptl4 | 1.854 | 3.859 | 7.069 | 0.0001249 | 0.1722 | 7:18627808-18634079:(-) |
| ENSRNOG00000024705 | Rarres2 | 2.479 | 4.591 | 6.783 | 0.0001748 | 0.1722 | 4:78205812-78208767:(-) |
| ENSRNOG00000015992 | Ccl20 | 4.455 | 0.1233 | 6.931 | 0.0001792 | 0.1722 | 9:88918433-88921001:(+) |
| ENSRNOG00000048321 | Tnfsf8 | 2.079 | 2.527 | 6.607 | 0.0001954 | 0.1722 | 5:79664765-79691258:(-) |
| ENSRNOG00000020953 | Ms4a7 | 1.993 | 2.448 | 6.576 | 0.0001987 | 0.1722 | 1:227490777-227506822:(-) |
| ENSRNOG00000062228 | LOC100359515 | 4.669 | 0.8863 | 6.785 | 0.0002066 | 0.1722 | 10:64762907-64790306:(-) |
| ENSRNOG00000002792 | Cxcl2 | 7.725 | 2.716 | 6.866 | 0.0002126 | 0.1722 | 14:18731378-18733391:(-) |
| ENSRNOG00000022839 | Ifit3 | 3.289 | 6.303 | 6.634 | 0.000217 | 0.1722 | 1:252906234-252911382:(+) |
| ENSRNOG00000028043 | Cxcl3 | 6.554 | 2.794 | 6.725 | 0.0002345 | 0.1722 | 14:18820168-18839595:(-) |
| ENSRNOG00000016812 | Adamts16 | 3.102 | 0.7835 | 6.528 | 0.0002355 | 0.1722 | 1:35067490-35198939:(+) |
Average expression against logFC. Confidently significant genes (FDR < 0.05) are indicated in red, whilst others are labelled to an FDR of 20.0%
Volcano plot with confidently significant genes shown in red. Genes to an FDR of20.0%are labelled.
Expression values for genes to an FDR of 20.0%. Genes are shown in order of significance. Jitter has been added to the x-axis
Gene Set Enrichment
Given the relatively small number of DE genes, the most suitable enrichment analysis was chosen to be GSEA as this works on a ranked list with no requirement for formal consideration of genes as differentially expressed. This approach walks through a ranked list and checks to see if genes with a given gene-set are clustered at either end of the ranked list. If no correlation between the gene-set and the experiment is evident, they should be evenly spread throughout the list. Otherwise they could reasonably be expected to show directional bias. As this is a fairly broad view analysis, gene sets were chosen as defined for human EntrezGene identifiers, then these were mapped to Ensembl rat identifiers. All gene-sets are described at the Broad Institute
Four gene sets were chosen:
- Hallmark gene-sets (k = 50): These are a small number of well defined, or high-level gene sets
- C2 Curated gene-sets (k = 4762): Gene sets curated from various sources such as online pathway databases, the biomedical literature, and knowledge of domain experts.
- C5 GO gene-sets (k = 5917): Gene sets that contain genes annotated by the same GO term
- C7 Immunologic Signature gene-sets (k = 4872): Gene sets that represent cell states and perturbations within the immune system. The signatures were generated by manual curation of published studies in human and mouse immunology.
Genes were ranked by t-statistic, and all results were permuted 1,000,000 times for greater precision in results.
An additional 3 points of caution should be noted:
- This method will almost always return results and may contain false positives. As a result, very strict p-value adjustment strategies will be used.
- There is an inherent bias towards defined pathways. Unknown pathways are simply not tested
- Many gene-sets will overlap considerably and untangling which are key pathways can be challenging
Hallmark Gene Sets
| Pathway | NES | \(p\) | \(p_{adj}\) | size | sizeAtMax | Genes |
|---|---|---|---|---|---|---|
| HALLMARK_INTERFERON_GAMMA_RESPONSE | 1.673 | 1.151e-06 | 5.757e-05 | 181 | 37 | Il6,Ccl7,Ifit3,Ptgs2,C1s,Isg15,Sod2,Tnfaip6,Socs3,Pde4b,Mx1,Cfb,Ifitm3,Upp1,Ccl2,Hif1a,Ifit2,Usp1… |
| HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION | 1.83 | 1.154e-06 | 5.769e-05 | 178 | 84 | Mmp3,Il6,Cxcl6,Tgfbi,Fbln2,Htra1,Acta2,Mgp,Ptx3,Itga5,Timp1,Mfap5,Col5a3,Fbn2,Col8a2,Col3a1,Tfpi2… |
| HALLMARK_TNFA_SIGNALING_VIA_NFKB | 1.928 | 1.156e-06 | 5.782e-05 | 175 | 87 | Il6,Tlr2,Cxcl6,Il1b,Ptgs2,Sod2,Junb,Tnfaip6,G0s2,F3,Socs3,Il1a,Pde4b,Olr1,Gfpt2,Ptx3,Ier3,Inhba,C… |
| HALLMARK_MYOGENESIS | 1.78 | 1.163e-06 | 5.816e-05 | 168 | 72 | LOC108348074,Mylpf,Myh8,Tnni2,Myoz1,Myl1,Myh1,Ckm,Myom2,Eno3,Tnnt3,Pgam2,Mb,Tnnc2,Cox6a2,Pvalb,Co… |
| HALLMARK_INFLAMMATORY_RESPONSE | 1.936 | 1.169e-06 | 5.845e-05 | 162 | 59 | Il6,Ccl7,Tlr2,Cxcl6,Il1b,Has2,Il1r1,Tnfaip6,F3,Il1a,Pde4b,Slc7a2,Olr1,Ifitm3,Mefv,Itga5,Timp1,Slc… |
| HALLMARK_COMPLEMENT | 1.697 | 2.336e-06 | 0.0001168 | 163 | 36 | Il6,Ctsl,C1s,Ctss,F3,Plat,Ctsc,Olr1,Cfb,Timp1,C1qa,Tfpi2,Col4a2,Maff,C2,Zfpm2,Pla2g7,Mmp14,Lgmn,P… |
| HALLMARK_COAGULATION | 1.777 | 3.802e-06 | 0.0001901 | 92 | 29 | Mmp3,Ctsl,Cfi,C1s,F3,Plat,Htra1,Olr1,Cfb,Timp1,C1qa,LOC100911545,Tfpi2,Maff,C2,Mmp2,Ctsk,Mmp14,Lg… |
| HALLMARK_HEME_METABOLISM | -2.074 | 7.816e-06 | 0.0003908 | 187 | 80 | Bpgm,Ctse,Mkrn1,Minpp1,Epb41,Fech,Foxo3,Klf1,Abcb6,LOC103689947,Ranbp10,Sptb,Alas2,Slc4a1,Tent5c,… |
| HALLMARK_MYC_TARGETS_V1 | -1.8 | 8.052e-06 | 0.0004026 | 193 | 45 | Pabpc4,Tufm,Pold2,Ccna2,Srsf2,Ran,Cdc20,Mcm5,Trim28,Impdh2,LOC100911372,Hnrnpd,Ncbp2,Lsm2,Mcm7,Cs… |
| HALLMARK_E2F_TARGETS | -2.283 | 8.337e-06 | 0.0004169 | 200 | 94 | Cdc25b,Birc5,Cdca3,E2f8,Ncapd2,H2afx,Kif18b,Stmn1,Rrm2,Pold2,Srsf2,Rad51ap1,Aurka,Ran,Dclre1b,Zw1… |
| HALLMARK_G2M_CHECKPOINT | -2.004 | 8.382e-06 | 0.0004191 | 201 | 70 | Hira,Cdc25b,Birc5,H2afx,Stmn1,Tent4a,Ccnf,Ccna2,Srsf2,Tgfb1,Aurka,Plk1,Cdc20,Mcm5,E2f4,Traip,Nek2… |
| HALLMARK_HYPOXIA | 1.56 | 6.203e-05 | 0.003102 | 184 | 57 | Il6,Angptl4,Errfi1,LOC100911515,Tgfbi,F3,Eno3,Pgam2,Ddit4,Slc2a1,Prdx5,Pygm,Tgm2,Ier3,Atf3,Plin2,… |
| HALLMARK_MITOTIC_SPINDLE | -1.528 | 8.382e-05 | 0.004191 | 201 | 31 | Epb41,Birc5,Sept9,Anln,Shroom2,Tubgcp2,Aurka,Plk1,Itsn1,Nek2,Racgap1,Arhgef12,Pcnt,Cep131,Tlk1,Mi… |
| HALLMARK_IL6_JAK_STAT3_SIGNALING | 1.667 | 9.709e-05 | 0.004854 | 79 | 24 | Il6,Ccl7,Tlr2,Il1b,Il1r2,Il1r1,Cxcl13,Socs3,LOC100911545,Cd14,Osmr,Il2ra,Tnfrsf12a,Il4r,LOC103694… |
Enrichment score for the most highly ranked hallmark geneset
C2 Curated Gene Sets
As this was a larger database of gene-sets, only those with between 15 & 500 genes were tested.
| Pathway | NES | \(p\) | \(p_{adj}\) | size | sizeAtMax | Genes |
|---|---|---|---|---|---|---|
| REN_ALVEOLAR_RHABDOMYOSARCOMA_DN | 1.769 | 1.051e-06 | 0.003658 | 400 | 116 | S100a6,Cttn,Ptges,C1s,Tspo,Tgfbi,Rhoc,Plat,Htra1,Ptx3,Itga5,Timp1,Col8a2,Tfpi2,Gpr176,Inhba,Osmr,… |
| NABA_MATRISOME_ASSOCIATED | 1.756 | 1.056e-06 | 0.003675 | 380 | 156 | Mmp3,Il6,Ccl7,Angptl4,Tnfsf8,Adamts16,Cxcl6,S100a6,Ctsl,C1qtnf7,Il1b,Slpi,Adamts15,Masp1,Adamts1,… |
| CHARAFE_BREAST_CANCER_LUMINAL_VS_BASAL_DN | 1.621 | 1.057e-06 | 0.003679 | 375 | 116 | Ifit3,Tlr2,Cfi,Ptgs2,Slpi,C1s,Tgfbi,F3,Gpm6b,Plat,Ctsc,Il1a,Htra1,Il1rap,Cfb,Nxn,Upp1,Col4a1,Inhb… |
| BOQUEST_STEM_CELL_CULTURED_VS_FRESH_UP | 1.679 | 1.058e-06 | 0.003683 | 371 | 120 | Angptl4,Cfi,LOC108348074,Prg4,Sod2,Junb,Tnfaip6,Ctss,Flnc,Mx1,Rrad,Agtr1a,Ifitm3,LOC102553715,Mgp… |
| SCHUETZ_BREAST_CANCER_DUCTAL_INVASIVE_UP | 1.736 | 1.075e-06 | 0.003741 | 318 | 117 | Mmp3,Rarres2,LOC108348074,C1s,Il1r1,Tnfaip6,Ctss,Tgfbi,Fbln2,Htra1,Olr1,Gfpt2,Mfap5,C1qa,LOC49827… |
| RICKMAN_TUMOR_DIFFERENTIATED_WELL_VS_POORLY_DN | 1.633 | 1.082e-06 | 0.003764 | 300 | 97 | S100a6,Adamts15,Gpm6b,Il1a,Htra1,Slc2a1,Col5a3,Upp1,Inhba,Hif1a,Rcan1,Tes,Cdkn1a,Trem1,Sdc1,Il20r… |
| WANG_SMARCE1_TARGETS_UP | 1.794 | 1.116e-06 | 0.003884 | 229 | 91 | Ifit3,Cfi,LOC108348074,Car4,C1s,Prg4,Il1r1,Tgfbi,Socs3,Plat,Acta2,Glipr2,Mgp,Ptx3,Mfap5,Col3a1,Ta… |
| HOSHIDA_LIVER_CANCER_SUBCLASS_S1 | 1.662 | 1.12e-06 | 0.003898 | 223 | 61 | LOC108348074,Ctss,Ccl3,AABR07034739.1,Ctsc,Acta2,Mgp,Slc2a1,Crabp2,LOC498276,Col3a1,Col4a1,Rin2,T… |
| BOQUEST_STEM_CELL_UP | 1.676 | 1.124e-06 | 0.00391 | 217 | 85 | C1s,Tnfaip6,F3,Htra1,Cfb,LOC102553715,Gfpt2,Mgp,Ptx3,Crabp2,Timp1,Mfap5,Col3a1,Col14a1,Inhba,Col1… |
| VERHAAK_GLIOBLASTOMA_MESENCHYMAL | 1.706 | 1.125e-06 | 0.003915 | 215 | 90 | Tlr2,Il1r1,Tgfbi,Ctsc,Itga5,Timp1,LOC498276,Col8a2,Dsc2,Sypl1,Cd14,Lamb1,Msr1,Folr2,Tes,Il4r,Bnc2… |
| MCLACHLAN_DENTAL_CARIES_UP | 1.673 | 1.128e-06 | 0.003926 | 210 | 73 | Il6,Cfi,Il1b,LOC108348074,Ptgs2,Slpi,Sod2,Cxcl13,Tnfaip6,Ctss,G0s2,Ccl3,Socs3,Ctsc,Cfb,Mgp,Timp1,… |
| MARKEY_RB1_ACUTE_LOF_DN | 1.67 | 1.133e-06 | 0.003944 | 203 | 55 | Mmp3,Ccl7,Ifit3,Isg15,Sod2,Tgfbi,Pcsk5,Ccl3,Socs3,RGD1559482,AABR07030793.1,Crabp2,Prdx5,LOC49827… |
| PICCALUGA_ANGIOIMMUNOBLASTIC_LYMPHOMA_UP | 1.725 | 1.147e-06 | 0.003992 | 186 | 67 | Rarres2,LOC108348074,C1s,Adamts1,Ccl21,Ctsc,Acta2,Cfb,LOC102553715,C1qa,Col8a2,LOC100911545,Col3a… |
| ZWANG_CLASS_3_TRANSIENTLY_INDUCED_BY_EGF | 1.686 | 1.15e-06 | 0.004001 | 183 | 68 | Il6,Il1b,Ptgs2,Junb,Nfkbiz,Socs3,Il1a,Olr1,Il1rap,Slc2a1,Pygm,Mmp19,Ier3,Atf3,Phlda1,Cebpd,Fos,Ma… |
| ZHANG_RESPONSE_TO_IKK_INHIBITOR_AND_TNF_UP | 1.746 | 1.154e-06 | 0.004016 | 178 | 54 | Ifit3,Tlr2,Il1b,Il1r2,Sod2,Tnfaip6,G0s2,Nfkbiz,Socs3,Il1a,Slc7a2,Olr1,Ptx3,Ddit4,Inhba,Atf3,Maff,… |
| BROWN_MYELOID_CELL_DEVELOPMENT_UP | 1.864 | 1.18e-06 | 0.004105 | 152 | 48 | Tlr2,S100a6,Il1b,Il1r2,Junb,Ctss,Tgfbi,Tnnt3,Olr1,Glipr2,LOC498276,Upp1,Cebpd,C2,Il1rn,Dgat2,Trem… |
| GROSS_HYPOXIA_VIA_ELK3_AND_HIF1A_UP | 1.733 | 1.199e-06 | 0.004172 | 135 | 50 | Angptl4,Errfi1,Vwa1,LOC100911515,Nfkbiz,Slc2a1,Prdx5,Atf3,Phlda1,Ankrd1,Fos,Rcan1,AC094212.1,Dusp… |
| NABA_ECM_REGULATORS | 1.771 | 1.2e-06 | 0.004176 | 134 | 61 | Mmp3,Adamts16,Ctsl,Slpi,Adamts15,Masp1,Adamts1,Ctss,Pcsk5,AABR07049085.1,Plat,Ctsc,Htra1,Tll1,Tgm… |
| MIKKELSEN_MEF_LCP_WITH_H3K4ME3 | 1.83 | 1.231e-06 | 0.004285 | 112 | 51 | Ccl7,Masp1,Col3a1,Mmp19,Inhba,Ccl2,Rufy4,LOC108348048,Nnmt,Ccn5,Mab21l3,Il1rn,Lum,Il18rap,Gal3st4… |
| ALTEMEIER_RESPONSE_TO_LPS_WITH_MECHANICAL_VENTILATION | 1.986 | 1.234e-06 | 0.004294 | 110 | 50 | Il6,Tlr2,Cxcl6,Il1b,Il1r2,Isg15,Sod2,Junb,Nfkbiz,Ccl3,F3,Socs3,Il1a,Gk,Clec4e,Mefv,Tgm1,Timp1,LOC… |
Enrichment score for the most highly ranked C2 genesets
C5 GO Gene Sets
For analysis of GO-related gene-sets the maximal set size was reduced to 200 to ensure the more generic & high-level terms were excluded.
| Pathway | NES | \(p\) | \(p_{adj}\) | size | sizeAtMax | Genes |
|---|---|---|---|---|---|---|
| GO_MUSCLE_SYSTEM_PROCESS | 1.925 | 1.141e-06 | 0.004073 | 194 | 73 | LOC100909761,Il1b,Agtr2,Mylpf,Myh8,LOC103692716,Mybpc2,Tnni2,Myl1,Myh1,Myom2,Tnnt3,Pgam2,Acta2,Ht… |
| GO_REGULATION_OF_ERK1_AND_ERK2_CASCADE | 1.708 | 1.159e-06 | 0.004138 | 173 | 46 | Il6,Ccl7,Il1b,Errfi1,Scimp,Ccl3,Ccl21,Havcr2,Glipr2,Ndrg4,Htr2b,Ccl2,Atf3,Bmper,LOC103689974,Tnfs… |
| GO_CONTRACTILE_FIBER | 1.926 | 1.168e-06 | 0.00417 | 163 | 63 | LOC100909761,Mylpf,Myh8,Abra,Tnni2,Myl1,Myh1,Myom2,Flnc,Tnnt3,Acta2,Pde4b,Tnnc2,Ankrd23,Cmya5,Tmo… |
| GO_MUSCLE_CONTRACTION | 1.974 | 1.172e-06 | 0.004183 | 160 | 67 | LOC100909761,Mylpf,Myh8,LOC103692716,Mybpc2,Tnni2,Myl1,Myh1,Myom2,Tnnt3,Pgam2,Acta2,Htr7,Mb,Tnnc2… |
| GO_CELLULAR_RESPONSE_TO_BIOTIC_STIMULUS | 1.716 | 1.194e-06 | 0.004264 | 139 | 42 | Il6,Tlr2,Il1b,Arg1,LOC497963,Tspo,Ccl3,Pde4b,Havcr2,Ccl2,Cd14,Hif1a,Ankrd1,Wnt5a,Irak2,Nlrp3,Lbp,… |
| GO_POSITIVE_REGULATION_OF_RESPONSE_TO_WOUNDING | 1.829 | 1.235e-06 | 0.004408 | 110 | 41 | Il6,Ccl7,Tlr2,Il1b,Ptgs2,Ccl3,F3,Agtr1a,Ccl2,Osmr,LOC103689974,Tnfsf11,Wnt5a,Lbp,Osm,Pla2g7,Ccl12… |
| GO_RESPONSE_TO_INTERFERON_GAMMA | 1.898 | 1.243e-06 | 0.004437 | 105 | 38 | Ccl7,LOC497963,Ccl3,Ccl21,Ifitm3,AC099450.1,Mefv,Ccl2,Oas1a,Wnt5a,Gbp2,Acod1,Icam1,Aqp4,Ccl12,Ccl… |
| GO_RESPONSE_TO_INTERLEUKIN_1 | 1.787 | 1.281e-06 | 0.004573 | 85 | 35 | Il6,Ccl7,Has2,Il1r1,Ccl3,Ccl21,Il1rl2,Ccl2,Hif1a,Ankrd1,Irak2,Acod1,Icam1,Ccl12,Ccl9,Ccl11,Cebpb,… |
| GO_CELLULAR_RESPONSE_TO_INTERFERON_GAMMA | 1.927 | 1.283e-06 | 0.00458 | 84 | 33 | Ccl7,LOC497963,Ccl3,Ccl21,AC099450.1,Ccl2,Oas1a,Wnt5a,Gbp2,Acod1,Icam1,Aqp4,Ccl12,Ccl9,Ccl11,Oas3… |
| GO_LEUKOCYTE_CHEMOTAXIS | 1.814 | 1.299e-06 | 0.004639 | 77 | 33 | Il6,Ccl7,Il1b,Cxcl13,Ccl3,Ccl21,Pde4b,Ccl2,LOC103689974,Tnfsf11,Folr2,Lbp,Trem1,Ccr5,Ccl12,Ccl9,C… |
| GO_POSITIVE_REGULATION_OF_INFLAMMATORY_RESPONSE | 1.873 | 1.304e-06 | 0.004655 | 75 | 35 | Il6,Ccl7,Tlr2,Il1b,Ptgs2,Ccl3,Agtr1a,Ccl2,Osmr,LOC103689974,Tnfsf11,Wnt5a,Lbp,Osm,Pla2g7,Ccl12,Cc… |
| GO_CELLULAR_RESPONSE_TO_INTERLEUKIN_1 | 1.888 | 1.332e-06 | 0.004757 | 64 | 31 | Il6,Ccl7,Has2,Il1r1,Ccl3,Ccl21,Il1rl2,Ccl2,Hif1a,Ankrd1,Irak2,Acod1,Icam1,Ccl12,Ccl9,Ccl11,Cebpb,… |
| GO_CYTOKINE_RECEPTOR_BINDING | 1.67 | 2.349e-06 | 0.008386 | 157 | 50 | Il6,Ccl7,Tnfsf8,Cxcl6,Il1b,Cxcl13,Ccl3,Ccl21,Il1a,Il1rap,Itga5,Nol3,Inhba,Ccl2,Rasl11b,LOC1036899… |
| GO_DEFENSE_RESPONSE_TO_VIRUS | 1.734 | 2.426e-06 | 0.00866 | 125 | 26 | Il6,Ifit3,Isg15,Ifit1bl,Mx1,Ifitm3,Ddit4,Oas1a,Pmaip1,Ifit2,Nlrp3,Dhx58,Cd8a,Mx2,Oas3,Oasl,Plscr1… |
| GO_REGULATION_OF_CYTOKINE_SECRETION | 1.736 | 2.502e-06 | 0.008933 | 100 | 38 | Il6,Tlr2,Il1b,Agtr2,Ccl3,Il1a,Ssc5d,Havcr2,Clec4e,Htr2b,Cd14,Wnt5a,Nlrp3,Il4r,Tlr1,Cd274,Postn,Cd… |
Enrichment score for the most highly ranked C5 genesets
C7 Immunologic Signatures
| Pathway | NES | \(p\) | \(p_{adj}\) | size | sizeAtMax | Genes |
|---|---|---|---|---|---|---|
| GSE42021_TCONV_PLN_VS_TREG_PRECURSORS_THYMUS_DN | 1.785 | 1.143e-06 | 0.00557 | 192 | 49 | Ccl7,Angptl4,Il1b,Errfi1,Ptgs2,Ptges,LOC103692716,Junb,Nfkbiz,Socs3,Il1a,Glipr2,Clec4e,LOC498276,… |
| GSE22935_WT_VS_MYD88_KO_MACROPHAGE_UP | 1.733 | 1.144e-06 | 0.005574 | 191 | 58 | Ptgs2,Icos,Tnn,Htra1,Mefv,Itga5,C1qa,Col16a1,Hif1a,LOC108348048,Tes,Ptgr1,Mmp2,M6pr,Emilin2,Vwa5a… |
| GSE22935_WT_VS_MYD88_KO_MACROPHAGE_48H_MBOVIS_BCG_STIM_DN | 1.658 | 1.145e-06 | 0.005577 | 190 | 57 | Aldh18a1,Il1r1,Rhoc,Socs3,Plat,C1qa,Col3a1,Brk1,Il1rl2,Crip1,Plin2,LOC108348048,Dpt,Tes,Vwa5a,Wls… |
| GSE42021_TREG_PLN_VS_CD24INT_TREG_THYMUS_UP | 1.838 | 1.145e-06 | 0.005577 | 190 | 59 | Il1b,Errfi1,Ptgs2,Ptges,LOC103692716,Junb,Nfkbiz,Socs3,Plat,Il1a,Glipr2,AABR07044940.1,Upp1,Cd14,… |
| GSE18281_SUBCAPSULAR_VS_CENTRAL_CORTICAL_REGION_OF_THYMUS_DN | 1.688 | 1.146e-06 | 0.005584 | 188 | 52 | Ifit3,Cxcl6,Ifit1bl,Nfkbiz,Socs3,Ctsc,Gk,Mefv,Itga5,Timp1,Upp1,Oas1a,Fos,Ifit2,Usp18,Samsn1,Cdkn1… |
| GSE22140_GERMFREE_VS_SPF_MOUSE_CD4_TCELL_DN | 1.668 | 1.148e-06 | 0.005592 | 186 | 45 | Ccl7,Tnfsf8,Ifit3,Il1b,Tgfbi,Ccl3,Htra1,Mx1,RGD1559482,AABR07030793.1,Ifitm3,AABR07044940.1,Ptx3,… |
| GSE27434_WT_VS_DNMT1_KO_TREG_DN | 1.809 | 1.148e-06 | 0.005592 | 186 | 44 | Ccl7,Il1b,Errfi1,Ptgs2,LOC103692716,Junb,Nfkbiz,Socs3,Il1a,Fbln2,Clec4e,Crabp2,Inhba,Cd14,Phlda1,… |
| GSE14769_UNSTIM_VS_60MIN_LPS_BMDM_DN | 1.705 | 1.149e-06 | 0.005597 | 185 | 53 | Ccl7,Tlr2,Il1b,Errfi1,Ptgs2,Sod2,F3,Clec4e,Ier3,Atf3,Cd14,Hif1a,Phlda1,Fos,Dusp16,Acod1,Icam1,Osm… |
| GSE3039_ALPHAALPHA_VS_ALPHABETA_CD8_TCELL_UP | 1.725 | 1.149e-06 | 0.0056 | 184 | 52 | Il6,Tlr2,Ctsl,Ptges,LOC100911515,Socs3,Htr7,Nxn,Slc2a1,LOC498276,Upp1,Ier3,Cd14,Phlda1,Maff,Uqcr1… |
| GSE22140_HEALTHY_VS_ARTHRITIC_GERMFREE_MOUSE_CD4_TCELL_DN | 1.678 | 1.15e-06 | 0.005604 | 183 | 57 | Ccl7,Tnfsf8,Ifit3,Tlr2,Il1b,Tgfbi,Ccl3,Mx1,AABR07044940.1,Ptx3,Timp1,LOC498276,Upp1,Ier3,Crip1,Pm… |
| GSE14769_UNSTIM_VS_40MIN_LPS_BMDM_DN | 1.757 | 1.152e-06 | 0.005612 | 181 | 53 | Ccl7,Tlr2,Errfi1,Ptgs2,Junb,Nfkbiz,Socs3,Pde4b,Clec4e,Ier3,Ccl2,Atf3,Cd14,Phlda1,Fos,Maff,Usp18,R… |
| GSE45365_NK_CELL_VS_CD11B_DC_DN | 1.786 | 1.154e-06 | 0.005621 | 179 | 54 | Il6,Il1b,Ptges,Junb,Nfkbiz,Pde4b,Olr1,Clec4e,Mefv,Ptx3,Timp1,Tgm2,Ier3,Cd14,Phlda1,Osmr,Fos,Maff,… |
| GSE14769_UNSTIM_VS_80MIN_LPS_BMDM_DN | 1.766 | 1.155e-06 | 0.005625 | 178 | 59 | Tlr2,Il1b,Isg15,Sod2,Adamts1,Clec4e,Tgm2,Atf3,Phlda1,Fos,Maff,Gbp2,Usp18,Arl5b,Il1rn,Acod1,Flrt3,… |
| GSE19198_CTRL_VS_IL21_TREATED_TCELL_24H_UP | 1.764 | 1.155e-06 | 0.005625 | 178 | 54 | Mmp3,Il6,Ccl7,Errfi1,Sod2,Il1a,Pde4b,Slc7a2,Ifitm3,Itga5,Timp1,Upp1,Tfpi2,Tgm2,Atf3,Hif1a,Plin2,M… |
| GSE46606_UNSTIM_VS_CD40L_IL2_IL5_1DAY_STIMULATED_IRF4HIGH_SORTED_BCELL_DN | 1.723 | 1.155e-06 | 0.005625 | 178 | 42 | Il6,Il1b,Errfi1,Ptgs2,Nfkbiz,Socs3,Pde4b,Rrad,Mefv,Ier3,Phlda1,Maff,Cdkn1a,Acod1,Icam1,Rgs1,Itpkc… |
| GSE34392_ST2_KO_VS_WT_DAY8_LCMV_EFFECTOR_CD8_TCELL_DN | 1.696 | 1.156e-06 | 0.00563 | 177 | 68 | Il6,Tlr2,Il1b,Isg15,Ifit1bl,Junb,Nfkbiz,F3,Slc7a2,Tll1,Slc2a1,Tgm2,Atf3,Phlda1,Ifit2,Usp18,Rcan1,… |
| GSE18281_CORTICAL_VS_MEDULLARY_THYMOCYTE_UP | 1.731 | 1.156e-06 | 0.005634 | 176 | 58 | Ifit3,Cxcl6,Arg1,Ptgs2,Slpi,Isg15,Ifit1bl,Ctss,Plat,Ctsc,Gk,Mx1,Ifitm3,Tgm2,Atf3,Cd14,Phlda1,Osmr… |
| GSE42021_TREG_PLN_VS_CD24HI_TREG_THYMUS_UP | 1.829 | 1.158e-06 | 0.005642 | 174 | 55 | Ccl7,Il1b,Errfi1,Ptgs2,LOC103692716,Junb,Nfkbiz,Socs3,Il1a,Fbln2,Clec4e,Crabp2,Upp1,Ier3,Cd14,Phl… |
| GSE7509_DC_VS_MONOCYTE_DN | 1.667 | 1.159e-06 | 0.005646 | 173 | 36 | Ifit3,Slpi,Ptges,Sod2,Tspo,Olr1,Clec4e,Inhba,Cd14,Gbp2,Usp18,Ccn5,Acod1,RGD1564937,Slfn4,Ets2,Anx… |
| GSE32986_CURDLAN_HIGHDOSE_VS_GMCSF_AND_CURDLAN_HIGHDOSE_STIM_DC_DN | 1.72 | 1.159e-06 | 0.005646 | 173 | 58 | Rarres2,Tlr2,Il1b,Ptgs2,Isg15,Sod2,Junb,Nfkbiz,Socs3,Il1a,Pde4b,Olr1,Rrad,Clec4e,Mefv,Itga5,Maff,… |
Enrichment score for the most highly ranked C7 genesets
Session Info
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
locale: LC_CTYPE=en_AU.UTF-8, LC_NUMERIC=C, LC_TIME=en_AU.UTF-8, LC_COLLATE=en_AU.UTF-8, LC_MONETARY=en_AU.UTF-8, LC_MESSAGES=en_AU.UTF-8, LC_PAPER=en_AU.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_AU.UTF-8 and LC_IDENTIFICATION=C
attached base packages: grid, stats4, parallel, stats, graphics, grDevices, utils, datasets, methods and base
other attached packages: fgsea(v.1.10.1), Rcpp(v.1.0.2), ggrepel(v.0.8.1), plyranges(v.1.4.3), rtracklayer(v.1.44.4), GenomicRanges(v.1.36.1), GenomeInfoDb(v.1.20.0), IRanges(v.2.18.2), S4Vectors(v.0.22.0), scales(v.1.0.0), ngsReports(v.1.1.1), BiocGenerics(v.0.30.0), pander(v.0.6.3), magrittr(v.1.5), forcats(v.0.4.0), stringr(v.1.4.0), dplyr(v.0.8.3), purrr(v.0.3.2), readr(v.1.3.1), tidyr(v.0.8.3), tibble(v.2.1.3), ggplot2(v.3.2.1), tidyverse(v.1.2.1), edgeR(v.3.26.8) and limma(v.3.40.6)
loaded via a namespace (and not attached): colorspace(v.1.4-1), hwriter(v.1.3.2), XVector(v.0.24.0), ggdendro(v.0.1-20), rstudioapi(v.0.10), lubridate(v.1.7.4), xml2(v.1.2.2), leaps(v.3.0), knitr(v.1.24), zeallot(v.0.1.0), jsonlite(v.1.6), Rsamtools(v.2.0.0), Cairo(v.1.5-10), broom(v.0.5.2), cluster(v.2.1.0), shiny(v.1.3.2), compiler(v.3.6.1), httr(v.1.4.1), backports(v.1.1.4), assertthat(v.0.2.1), Matrix(v.1.2-17), lazyeval(v.0.2.2), cli(v.1.1.0), later(v.0.8.0), htmltools(v.0.3.6), tools(v.3.6.1), gtable(v.0.3.0), glue(v.1.3.1), GenomeInfoDbData(v.1.2.1), reshape2(v.1.4.3), FactoMineR(v.1.42), ShortRead(v.1.42.0), fastmatch(v.1.1-0), Biobase(v.2.44.0), cellranger(v.1.1.0), vctrs(v.0.2.0), Biostrings(v.2.52.0), nlme(v.3.1-141), crosstalk(v.1.0.0), xfun(v.0.9), rvest(v.0.3.4), mime(v.0.7), XML(v.3.98-1.20), zlibbioc(v.1.30.0), MASS(v.7.3-51.4), zoo(v.1.8-6), promises(v.1.0.1), hms(v.0.5.1), SummarizedExperiment(v.1.14.1), rhdf5(v.2.28.0), RColorBrewer(v.1.1-2), yaml(v.2.2.0), gridExtra(v.2.3), latticeExtra(v.0.6-28), stringi(v.1.4.3), highr(v.0.8), BiocParallel(v.1.18.1), truncnorm(v.1.0-8), rlang(v.0.4.0), pkgconfig(v.2.0.2), bitops(v.1.0-6), matrixStats(v.0.55.0), evaluate(v.0.14), lattice(v.0.20-38), Rhdf5lib(v.1.6.0), GenomicAlignments(v.1.20.1), htmlwidgets(v.1.3), labeling(v.0.3), tidyselect(v.0.2.5), plyr(v.1.8.4), R6(v.2.4.0), generics(v.0.0.2), DelayedArray(v.0.10.0), pillar(v.1.4.2), haven(v.2.1.1), withr(v.2.1.2), scatterplot3d(v.0.3-41), RCurl(v.1.95-4.12), modelr(v.0.1.5), crayon(v.1.3.4), plotly(v.4.9.0), rmarkdown(v.1.15), locfit(v.1.5-9.1), readxl(v.1.3.1), data.table(v.1.12.2), digest(v.0.6.20), flashClust(v.1.01-2), webshot(v.0.5.1), xtable(v.1.8-4), httpuv(v.1.5.1), munsell(v.0.5.0), viridisLite(v.0.3.0) and kableExtra(v.1.1.0)